OpenRaiser/Intern-Atlas huggingface dataset:
5G NR Positioning Enhancements in 3GPP Release-18
Venue: arXiv cs.IT 2024
Year: 2024

New radio (NR) positioning in the Third Generation Partnership Project (3GPP) Release 18 (Rel-18) enables 5G-advanced networks to achieve ultra-high accuracy positioning without dependence on global navigation satellite systems (GNSS) with key enablers such as the carrier phase positioning technique, standardized for the first time in a cellular communications standard and setting a new baseline for future generations. In addition, Rel-18 NR supports positioning functionalities for reduced capability (RedCap) user equipment and bandwidth aggregation for positioning measurements. Moreover, the low power solutions are designed for low power high accuracy positioning use cases. Lastly, sidelink-based positioning is introduced in Rel-18. This article constitutes a comprehensive treatment of the Rel-18 NR positioning enhancements crucial for the development of next-generation networks.
Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data
Venue: arXiv cs.CL 2024
Year: 2024

Large Vision-Language Models (VLMs) have demonstrated impressive performance on complex tasks involving visual input with natural language instructions. However, it remains unclear to what extent capabilities on natural images transfer to Earth observation (EO) data, which are predominantly satellite and aerial images less common in VLM training data. In this work, we propose a comprehensive benchmark to gauge the progress of VLMs toward being useful tools for EO data by assessing their abilities on scene understanding, localization and counting, and change detection tasks. Motivated by real-world applications, our benchmark includes scenarios like urban monitoring, disaster relief, land use, and conservation. We discover that, although state-of-the-art VLMs like GPT-4V possess extensive world knowledge that leads to strong performance on open-ended tasks like location understanding and image captioning, their poor spatial reasoning limits usefulness on object localization and counting tasks. Our benchmark will be made publicly available at https://vleo.danielz.ch/ and on Hugging Face at https://huggingface.co/collections/mit-ei/vleo-benchmark-datasets-65b789b0466555489cce0d70 for easy model evaluation.
Ambush from All Sides: Understanding Security Threats in Open-Source Software CI/CD Pipelines
Venue: arXiv cs.CR 2024
Year: 2024

The continuous integration and continuous deployment (CI/CD) pipelines are widely adopted on Internet hosting platforms, such as GitHub. With the popularity, the CI/CD pipeline faces various security threats. However, current CI/CD pipelines suffer from malicious code and severe vulnerabilities. Even worse, people have not been fully aware of its attack surfaces and the corresponding impacts. Therefore, in this paper, we conduct a large-scale measurement and a systematic analysis to reveal the attack surfaces of the CI/CD pipeline and quantify their security impacts. Specifically, for the measurement, we collect a data set of 320,000+ CI/CD pipeline-configured GitHub repositories and build an analysis tool to parse the CI/CD pipelines and extract security-critical usages. Besides, current CI/CD ecosystem heavily relies on several core scripts, which may lead to a single point of failure. While the CI/CD pipelines contain sensitive information/operations, making them the attacker's favorite targets. Inspired by the measurement findings, we abstract the threat model and the attack approach toward CI/CD pipelines, followed by a systematic analysis of attack surfaces, attack strategies, and the corresponding impacts. We further launch case studies on five attacks in real-world CI/CD environments to validate the revealed attack surfaces. Finally, we give suggestions on mitigating attacks on CI/CD scripts, including securing CI/CD configurations, securing CI/CD scripts, and improving CI/CD infrastructure.
Commit Messages in the Age of Large Language Models
Venue: arXiv cs.SE 2024
Year: 2024

Commit messages are explanations of changes made to a codebase that are stored in version control systems. They help developers understand the codebase as it evolves. However, writing commit messages can be tedious and inconsistent among developers. To address this issue, researchers have tried using different methods to automatically generate commit messages, including rule-based, retrieval-based, and learning-based approaches. Advances in large language models offer new possibilities for generating commit messages. In this study, we evaluate the performance of OpenAI's ChatGPT for generating commit messages based on code changes. We compare the results obtained with ChatGPT to previous automatic commit message generation methods that have been trained specifically on commit data. Our goal is to assess the extent to which large pre-trained language models can generate commit messages that are both quantitatively and qualitatively acceptable. We found that ChatGPT was able to outperform previous Automatic Commit Message Generation (ACMG) methods by orders of magnitude, and that, generally, the messages it generates are both accurate and of high-quality. We also provide insights, and a categorization, for the cases where it fails.
Generative AI to Generate Test Data Generators
Venue: arXiv cs.SE 2024
Year: 2024

Generating fake data is an essential dimension of modern software testing, as demonstrated by the number and significance of data faking libraries. Yet, developers of faking libraries cannot keep up with the wide range of data to be generated for different natural languages and domains. In this paper, we assess the ability of generative AI for generating test data in different domains. We design three types of prompts for Large Language Models (LLMs), which perform test data generation tasks at different levels of integrability: 1) raw test data generation, 2) synthesizing programs in a specific language that generate useful test data, and 3) producing programs that use state-of-the-art faker libraries. We evaluate our approach by prompting LLMs to generate test data for 11 domains. The results show that LLMs can successfully generate realistic test data generators in a wide range of domains at all three levels of integrability.
3DA: Assessing 3D-Printed Electrodes for Measuring Electrodermal Activity
Venue: arXiv cs.HC 2024
Year: 2024

Electrodermal activity (EDA) reflects changes in skin conductance, which are closely tied to human psychophysiological states. For example, EDA sensors can assess stress, cognitive workload, arousal, or other measures tied to the sympathetic nervous system for interactive human-centered applications. Yet, current limitations involve the complex attachment and proper skin contact with EDA sensors. This paper explores the concept of 3D printing electrodes for EDA measurements, integrating sensors into arbitrary 3D-printed objects, alleviating the need for complex assembly and attachment. We examine the adaptation of conventional EDA circuits for 3D-printed electrodes, assessing different electrode shapes and their impact on the sensing accuracy. A user study (N=6) revealed that 3D-printed electrodes can measure EDA with similar accuracy, suggesting larger contact areas for improved precision. We derive design implications to facilitate the integration of EDA sensors into 3D-printed devices to foster diverse integration into everyday objects for prototyping physiological interfaces.
Aesthetic Preference Prediction in Interior Design: Fuzzy Approach
Venue: arXiv cs.AI 2024
Year: 2024

Interior design is all about creating spaces that look and feel good. However, the subjective nature of aesthetic preferences presents a significant challenge in defining and quantifying what makes an interior design visually appealing. The current paper addresses this gap by introducing a novel methodology for quantifying and predicting aesthetic preferences in interior design. Our study combines fuzzy logic with image processing techniques. We collected a dataset of interior design images from social media platforms, focusing on essential visual attributes such as color harmony, lightness, and complexity. We integrate these features using weighted average to compute a general aesthetic score. Our approach considers individual color preferences in calculating the overall aesthetic preference. We initially gather user ratings for primary colors like red, brown, and others to understand their preferences. Then, we use the pixel count of the top five dominant colors in the image to get the color scheme preference. The color scheme preference and the aesthetic score are then passed as inputs to the fuzzy inference system to calculate an overall preference score. This score represents a comprehensive measure of the user's preference for a particular interior design, considering their color choices and general aesthetic appeal. We used the 2AFC (Two-Alternative Forced Choice) method to validate our methodology, achieving a notable hit rate of 0.7. This study can help designers and professionals better understand and meet people's interior design preferences, especially in a world that relies heavily on digital media.
3D-Plotting Algorithm for Insects using YOLOv5
Venue: arXiv cs.CV 2024
Year: 2024

In ecological research, accurately collecting spatiotemporal position data is a fundamental task for understanding the behavior and ecology of insects and other organisms. In recent years, advancements in computer vision techniques have reached a stage of maturity where they can support, and in some cases, replace manual observation. In this study, a simple and inexpensive method for monitoring insects in three dimensions (3D) was developed so that their behavior could be observed automatically in experimental environments. The main achievements of this study have been to create a 3D monitoring algorithm using inexpensive cameras and other equipment to design an adjusting algorithm for depth error, and to validate how our plotting algorithm is quantitatively precise, all of which had not been realized in conventional studies. By offering detailed 3D visualizations of insects, the plotting algorithm aids researchers in more effectively comprehending how insects interact within their environments.
Challenges in Understanding the Relationship between Teamwork Quality and Project Success in Large-Scale Agile Projects
Venue: arXiv cs.SE 2024
Year: 2024

A number of methods for large-scale agile development have recently been suggested. Much of the advice in agile methods focuses on teamwork. Prior research has established that teamwork quality influences project success both for traditional software development teams and agile teams. Further, prior studies have also suggested that teamwork quality may play out differently in large projects compared to small. We investigated the relationship between teamwork quality and project success with a survey of 196 project participants across 34 teams in four projects, replicating a previous study on single teams. The new data do not fit the previously established theoretical model, which raises several concerns. The observed effect of teamwork quality on project success operates differently across projects. We discuss possible reasons, which include disagreements on what characterises success in large-scale agile development, "concept drift" of teamwork quality factors, the possibility that interteam factors might have more influence on project success than intrateam factors, and finally, that our study design does not capture all relevant levels and functions. We conclude with a call for more studies on the quality and frequency of interaction between teams in addition to internal team factors to further advance theory and practice within large-scale agile software development.
Efficient Shape Formation by 3D Hybrid Programmable Matter: An Algorithm for Low Diameter Intermediate Structures
Venue: arXiv cs.DS 2024
Year: 2024

This paper considers the shape formation problem within the 3D hybrid model, where a single agent with a strictly limited viewing range and the computational capacity of a deterministic finite automaton manipulates passive tiles through pick-up, movement, and placement actions. The goal is to reconfigure a set of tiles into a specific shape termed an icicle. The icicle, identified as a dense, hole-free structure, is strategically chosen to function as an intermediate shape for more intricate shape formation tasks. It is designed for easy exploration by a finite state agent, enabling the identification of tiles that can be lifted without breaking connectivity. Compared to the line shape, the icicle presents distinct advantages, including a reduced diameter and the presence of multiple removable tiles. We propose an algorithm that transforms an arbitrary initially connected tile structure into an icicle in $\mathcal{O}(n^3)$ steps, matching the runtime of the line formation algorithm from prior work. Our theoretical contribution is accompanied by an extensive experimental analysis, indicating that our algorithm decreases the diameter of tile structures on average.
Model-driven development of data intensive applications over cloud resources
Venue: arXiv cs.DC 2024
Year: 2024

The proliferation of sensors over the last years has generated large amounts of raw data, forming data streams that need to be processed. In many cases, cloud resources are used for such processing, exploiting their flexibility, but these sensor streaming applications often need to support operational and control actions that have real-time and low-latency requirements that go beyond the cost effective and flexible solutions supported by existing cloud frameworks, such as Apache Kafka, Apache Spark Streaming, or Map-Reduce Streams. In this paper, we describe a model-driven and stepwise refinement methodological approach for streaming applications executed over clouds. The central role is assigned to a set of Petri Net models for specifying functional and non-functional requirements. They support model reuse, and a way to combine formal analysis, simulation, and approximate computation of minimal and maximal boundaries of non-functional requirements when the problem is either mathematically or computationally intractable. We show how our proposal can assist developers in their design and implementation decisions from a performance perspective. Our methodology allows to conduct performance analysis: The methodology is intended for all the engineering process stages, and we can (i) analyse how it can be mapped onto cloud resources, and (ii) obtain key performance indicators, including throughput or economic cost, so that developers are assisted in their development tasks and in their decision taking. In order to illustrate our approach, we make use of the pipelined wavefront array.
Double InfoGAN for Contrastive Analysis
Venue: arXiv cs.CV 2024
Year: 2024

Contrastive Analysis (CA) deals with the discovery of what is common and what is distinctive of a target domain compared to a background one. This is of great interest in many applications, such as medical imaging. Current state-of-the-art (SOTA) methods are latent variable models based on VAE (CA-VAEs). However, they all either ignore important constraints or they don't enforce fundamental assumptions. This may lead to sub-optimal solutions where distinctive factors are mistaken for common ones (or viceversa). Furthermore, the generated images have a rather poor quality, typical of VAEs, decreasing their interpretability and usefulness. Here, we propose Double InfoGAN, the first GAN based method for CA that leverages the high-quality synthesis of GAN and the separation power of InfoGAN. Experimental results on four visual datasets, from simple synthetic examples to complex medical images, show that the proposed method outperforms SOTA CA-VAEs in terms of latent separation and image quality. Datasets and code are available online.
SDRDPy: An application to graphically visualize the knowledge obtained with supervised descriptive rule algorithms
Venue: arXiv cs.AI 2024
Year: 2024

SDRDPy is a desktop application that allows experts an intuitive graphic and tabular representation of the knowledge extracted by any supervised descriptive rule discovery algorithm. The application is able to provide an analysis of the data showing the relevant information of the data set and the relationship between the rules, data and the quality measures associated for each rule regardless of the tool where algorithm has been executed. All of the information is presented in a user-friendly application in order to facilitate expert analysis and also the exportation of reports in different formats.
Graph Transformers without Positional Encodings
Venue: arXiv cs.LG 2024
Year: 2024

Recently, Transformers for graph representation learning have become increasingly popular, achieving state-of-the-art performance on a wide-variety of graph datasets, either alone or in combination with message-passing graph neural networks (MP-GNNs). Infusing graph inductive-biases in the innately structure-agnostic transformer architecture in the form of structural or positional encodings (PEs) is key to achieving these impressive results. However, designing such encodings is tricky and disparate attempts have been made to engineer such encodings including Laplacian eigenvectors, relative random-walk probabilities (RRWP), spatial encodings, centrality encodings, edge encodings etc. In this work, we argue that such encodings may not be required at all, provided the attention mechanism itself incorporates information about the graph structure. We introduce Eigenformer, a Graph Transformer employing a novel spectrum-aware attention mechanism cognizant of the Laplacian spectrum of the graph, and empirically show that it achieves performance competetive with SOTA Graph Transformers on a number of standard GNN benchmarks. Additionally, we theoretically prove that Eigenformer can express various graph structural connectivity matrices, which is particularly essential when learning over smaller graphs.
Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive Learning
Venue: arXiv cs.LG 2024
Year: 2024

Contrastive representation learning is crucial in time series analysis as it alleviates the issue of data noise and incompleteness as well as sparsity of supervision signal. However, existing constrastive learning frameworks usually focus on intral-temporal features, which fails to fully exploit the intricate nature of time series data. To address this issue, we propose DE-TSMCL, an innovative distillation enhanced framework for long sequence time series forecasting. Specifically, we design a learnable data augmentation mechanism which adaptively learns whether to mask a timestamp to obtain optimized sub-sequences. Then, we propose a contrastive learning task with momentum update to explore inter-sample and intra-temporal correlations of time series to learn the underlying structure feature on the unlabeled time series. Meanwhile, we design a supervised task to learn more robust representations and facilitate the contrastive learning process. Finally, we jointly optimize the above two tasks. By developing model loss from multiple tasks, we can learn effective representations for downstream forecasting task. Extensive experiments, in comparison with state-of-the-arts, well demonstrate the effectiveness of DE-TSMCL, where the maximum improvement can reach to 27.3%.
The 1/3-conjectures for domination in cubic graphs
Venue: arXiv cs.DM 2024
Year: 2024

A set S of vertices in a graph G is a dominating set of G if every vertex not in S is adjacent to a vertex in S . The domination number of G, denoted by $γ$(G), is the minimum cardinality of a dominating set in G. In a breakthrough paper in 2008, L{ö}wenstein and Rautenbach proved that if G is a cubic graph of order n and girth at least 83, then $γ$(G) $\le$ n/3. A natural question is if this girth condition can be lowered. The question gave birth to two 1/3-conjectures for domination in cubic graphs. The first conjecture, posed by Verstraete in 2010, states that if G is a cubic graph on n vertices with girth at least 6, then $γ$(G) $\le$ n/3. The second conjecture, first posed as a question by Kostochka in 2009, states that if G is a cubic, bipartite graph of order n, then $γ$(G) $\le$n/3. In this paper, we prove Verstraete's conjecture when there is no 7-cycle and no 8-cycle, and we prove the Kostochka's related conjecture for bipartite graphs when there is no 4-cycle and no 8-cycle.
Neural Machine Translation for Malayalam Paraphrase Generation
Venue: arXiv cs.CL 2024
Year: 2024

This study explores four methods of generating paraphrases in Malayalam, utilizing resources available for English paraphrasing and pre-trained Neural Machine Translation (NMT) models. We evaluate the resulting paraphrases using both automated metrics, such as BLEU, METEOR, and cosine similarity, as well as human annotation. Our findings suggest that automated evaluation measures may not be fully appropriate for Malayalam, as they do not consistently align with human judgment. This discrepancy underscores the need for more nuanced paraphrase evaluation approaches especially for highly agglutinative languages.
SAT-Based Subsumption Resolution
Venue: arXiv cs.LO 2024
Year: 2024

Subsumption resolution is an expensive but highly effective simplifying inference for first-order saturation theorem provers. We present a new SAT-based reasoning technique for subsumption resolution, without requiring radical changes to the underlying saturation algorithm. We implemented our work in the theorem prover Vampire, and show that it is noticeably faster than the state of the art.
Propagation Dynamics of Rumor vs. Non-rumor across Multiple Social Media Platforms Driven by User Characteristics
Venue: arXiv cs.SI 2024
Year: 2024

Studying information propagation dynamics in social media can elucidate user behaviors and patterns. However, previous research often focuses on single platforms and fails to differentiate between the nuanced roles of source users and other participants in cascades. To address these limitations, we analyze propagation cascades on Twitter and Weibo combined with a crawled dataset of nearly one million users with authentic attributes. Our preliminary findings from multiple platforms robustly indicate that rumors tend to spread more deeply, while non-rumors distribute more broadly. Interestingly, we discover that the spread of rumors is slower, persists longer, and, in most cases, involves fewer participants than that of non-rumors. And an undiscovered highlight is that reputable active users, termed `onlookers', inadvertently or unwittingly spread rumors due to their extensive online interactions and the allure of sensational fake news. Conversely, celebrities exhibit caution, mindful of releasing unverified information. Additionally, we identify cascade features aligning with exponential patterns, highlight the Credibility Erosion Effect (CEE) phenomenon in the propagation process, and discover the different contents and policies between the two platforms. Our findings enhance current understanding and provide a valuable statistical analysis for future research.